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The Research Council of Norway and the EU's framework program, Horizon Europe, require projects to have a data management plan (DMP). Additionally, The University of Bergen Policy for Open Science states that all research projects led by ÐÒÔË·Éͧ¼Æ»® must develop a data management plan.

The purpose of the data management plan is to outline how research data will be secured, both during the project and for future reuse. A data management plan is also useful for identifying the costs associated with data management and storage, as well as planning how these costs can be covered.

The researcher should establish a DMP early in the project, in line with funder and institutional requirements:
The DMP is a living document and should be updated as needed throughout the project.

 describes the data management plan as a living document that should accompany the research project. It should detail which data will be generated, how the data will be described, where the data will be stored, and if and how it may be shared.

The content of a data management plan will vary across disciplines, but 's practical guide to data management outlines the following key points:

1. Description of Data, Data Collection, and Reuse of Existing Data

   • How will new data be generated/collected and/or how will existing data be reused?
   • What data (type, format, and volume) will be generated or collected?

2. Documentation and Data Quality

   • What and (e.g., method descriptions or data organization) will accompany the data?
   • What data quality control measures will be implemented?

3. Storage and Backup During the Research Process

   • How will data and metadata be stored and backed up during the research process?
   • How will data security and the protection of any sensitive data be ensured?

4. Legal and Ethical Requirements and Guidelines

   • If personal data is processed, how will compliance with data protection and security legislation be ensured?
   • How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation applies?
   • How will potential ethical issues be addressed, and what ethical guidelines are followed?

5. Data Sharing and Long-Term Preservation

   • How and when will the data be shared? Are there potential restrictions on data sharing or reasons to place an embargo on the data?
   • How will data for preservation be selected, and where will it be stored for the long term (e.g., in a data archive)?
   • What tools or software will be necessary to access and use the data?
   • How will the application of a unique and persistent identifier (such as a Digital Object Identifier (DOI)) for each dataset be ensured?

6. Responsibility for Data Management and Resources

   • Who (e.g., role, position, and institution) will be responsible for data management?
   • What resources (e.g., budget and time) will be allocated to data management to ensure the data will be (Findable, Accessible, Interoperable, Re-usable)?

For more detailed information, see the Norwegian resource .

Using a digital data management planning tool helps to develop a DMP that complies with funder requirements and can easily the updated as the research project progresses. These plans can also be shared and edited by others.

FAIR Wizard Norway (Feide login):

From spring 2025, ÐÒÔË·Éͧ¼Æ»® users have access to the DMP tool FAIR Wizard Norway. This pilot is accompanied by resources from the ÐÒÔË·Éͧ¼Æ»®-led project .

Other DMP tools:

• from Digital Curation Centre (NB! Use the "Science Europe" template under Reference > Funder Requirements)
•  from Elixir (for life science projects, there are templates adapted for Norway that support exporting a machine-readable data management plan)
• from Sigma2/Sikt (adapted for Norway and support exporting a machine-readable data management plan)
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The Research Council encourages open publishing of DMPs. This can be achieved by downloading the developed DMP from the tool used and publishing it on platforms such as 

from Digital Curation Centre
 from University of Vienna
from LIBER Europe

In addition, there are many examples of data management plans available on platforms such as or on the websites of and . Note: These plans are not curated or quality-assured in any way and should not be used as templates.

What is research data?

Research data can be defined as all data produced or collected by the researcher in a research project. This includes information, particularly facts or numbers, gathered or generated to support claims in the literature, such as statistics, results of experiments, measurements, fieldwork observations, surveys, interview recordings, images, etc.

What are the FAIR principles?

The principles are a set of guidelines for open research data, standing for:

• Findable - data should be discoverable.
• Accessible - data should be easily accessible.
• Interoperable - data should be able to be opened, understood, combined, reused, and processed without restrictions, both now and in the future.
• Reusable - data should be reusable.

For research data to be reusable, its quality must be high. Therefore, both data and metadata must be findable, accessible, and interoperable.

Where can I archive and share my research data?

To make your research data visible and accessible, you should choose a discipline-specific archive. Such archives can be found on , which is the largest and most comprehensive registry of available data repositories. An alternative is .

If you cannot find an appropriate discipline-specific archive, as a researcher at the University of Bergen (ÐÒÔË·Éͧ¼Æ»®), you can archive your data in .

Please also see our page on Open access to research data.

What is metadata?

Metadata is structured information that describes, explains, locates, and makes it easier to retrieve and use a source of information. In short: data about the data. To help make your data reusable and accessible for you and others in the future, you must create and archive accurate metadata alongside your data.

The Digital Curation Centre (DCC) provides an overview of .

Do I have to make all my research data openly available?

Not necessarily. You must decide which data to store, which data you want to keep, which data should not be openly available, and which requirements apply from funders, the university, and/or any legal or regulatory requirements.

If there are no specific requirements, we recommend researchers consider the following: Which data are necessary to reproduce or validate the results? Note that this may include code. Which data have potential for reuse by others?

The Digital Curation Centre (DCC) offers useful guidance: ‘’

Are there "best practices" for handling research data?

The following components are necessary for good management of research data:

• Use short and descriptive
• Choose archival-worthy
• Track different  of your documents
• Create for each experiment or analysis

See for or for more information.

What are archival-worthy formats?

Archival-worthy file formats ensure that your data can be read by anyone, including in the future. Certain file formats are more likely to remain readable over time than others. Such formats are typically:

• Non-proprietary
• Open, with documented international standards
• Widely used in the research community
• Use standard character encoding, preferably Unicode (e.g., UTF-8)
• Uncompressed

When archiving your data, ensure you upload your files in an in addition to the original file format. Also, make sure all your files contain a valid file extension, such as .txt, .pdf. More information and examples of archival-worthy formats can be found at the Dutch national expert center for archiving research data: